Statistical physics has been developed in order to study the
collective behavior of many-particle systems. Traditionally, it aims
at understanding physical materials composed out of a large number of
molecules or other small constituents of matter. Probability theory is
combined with microscopic rules (interactions) to describe the collective properties such as the heat capacity, the density or various phase
transitions. Motivated by the demands of our developing society,
today's science needs to understand many other systems composed of a large number of interacting elements. To give few examples these elements may be: agents selling and buying
items on the market, genes regulating the cell functions or
dys-functions, the network of power lines delivering energy to our
homes, Boolean variables in logical formulas designed to verify
functions of electronic devices, planes landing and departing in a
large international airport, or the network of neurons in our brain
that allows one to read and comprehend this very text.

The experience collected over decades by physicists in the studies
of matter needs to be exploited in order to understand, or predict,
new relations between the microscopic (local) and macroscopic (global)
properties of these complex systems. Particularly handy in this task
are techniques developed in studies of disordered systems such as spin
glasses, structural glasses, interfaces pinned by impurities, polymer
networks or grains of sand. This is because the above mentioned
complex systems are very rarely ordered, homogeneous or strongly
symmetric, often they are not even embedded in an Euclidean space.
This is the general line of research that makes me passionate and that
I develop.